WILO : Wheel Inertial LiDAR Odometry : Multi-modal state estimation for an autonomous delivery device
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hdl:2117/373509
Tipus de documentProjecte Final de Màster Oficial
Data2022-07-19
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Abstract
The last mile of a product delivery accounts for more than half of its total transportation cost. The vehicles that currently carry out these tasks are major causes of congestion and pollution in cities. Autonomous delivery devices are an environmentally friendly solution that tackle the aforementioned issues. The first challenge any autonomous vehicle needs to overcome is answering the questions: where is the robot? and where is it going? These questions are solved by estimating the localization in space through the use of the sensors available on the robot. An accurate, fast, and smooth state estimation is a fundamental prerequisite for any operation. This work parts from an existing localization framework of an autonomous delivery device that relied on the use of wheel encoders, GNSS, and a inertial measurement unit (IMU) to estimate the position and orientation of the robot in space. Significant drift from IMU bias and wheel slippage accumulated over a short time, demanding constant and accurate GNSS cor- rections to avoid collisions. Urban environments usually suffer from poor satellite coverage due to buildings blocking the signals, causing inaccurate and slow readings. This work aims at incorporating LiDAR sensors, previously unused into the robot’s existing loosely-coupled localization framework. To this end, a state-of-the-art LiDAR-inertial state estimation algorithm is integrated, alongside the wheel encoders and IMU, to improve the state estimate. The localization estimation is evaluated in three real world sites: a controlled environment in the UPC-Barcelona campus, a LiDAR degraded parking-lot in Esplugues de Llobregat, and a representative environment of a last mile delivery operation in the neighbourhoods of l’Hospitalet de Llobregat. The final localization error after travelling 500m is reduced from 90m to less than 30cm of error after incorporating the LiDAR sensor into the state estimation; drastically reducing the need of constant and accurate GNSS readings. A limitation of the LiDAR localization method chosen is that it does not report the estimation error. In a feature-poor environment any LiDAR-based state estimation method will not be able to find a unique solution. Identifying when this degradation occurs is crucial for an accurate estimation of the state. In this work we propose and test an observability metric, based on the current LiDAR scan, that evaluates the information richness of the LiDAR measurements by extracting planar features from the scan and assessing the distribution of the plane normal vectors. This observability metric is tested out in a hallway scenario, where the degradation in localization along the hallway direction is correctly identified
MatèriesAutonomous vehicles -- Design and construction, Mobile robots -- Software -- Design and construction, Optoelectronic devices -- Calibration -- Mathematical models, Vehicles autònoms -- Disseny i construcció, Robots mòbils -- -- Programari -- Disseny i construcció, Dispositius optoelectrònics -- Calibratge -- Models matemàtics
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tfm-carlos-fernandez.pdf | 30,57Mb | Accés restringit |